[pt] Este trabalho propõe um modelo de implementação de um leilão bidimensional para concessões de serviços públicos. O desenho do leilão é feito pelo governo através de uma regra de pontuação quase-linear que valora o preço cobrado e o tempo para iniciar a prestação de serviços. Este modelo aplica-se ao conjunto de serviços públicos que geram grandes benefícios quando começam a ser prestado em uma data limite reduzida. Os potenciais compradores possuem informação privada sobre seus custos de produção e redução do tempo. A regra de pontuação reduz a dimensionalidade dos lances tornando-os unidimensionais para os participantes, o maior lance resulta em uma obrigação contratual ao vencedor. O modelo auxilia na elaboração do design do leilão de forma a maximizar as preferências do governo dado o comportamento estratégico dos compradores. / [en] In this thesis we propose a model for a two-dimensional auction of public service concession agreements. The government design of the auction involves an almost linear scoring rule that evaluates the price charged and the time to start providing the services. The model applies to the public services that improve social welfare by reducing the delivery time of services. Suppliers have private information about their costs and time reduction offer. The proposed scoring rule of each supplier reduces the dimensionality of the bids submitted to a single dimension. The winner is committed to his bid and obliges to provide the required services. The model assists in preparing the design of the auction in order to maximize the preferences of the government given to the strategic behavior of buyers.

Box-Behnken designs are used to estimate parameters in a second-order response surface model (Box and Behnken, 1960). These designs are formed by combining ideas from incomplete block designs (BIBD or PBIBD) and factorial experiments, specifically 2^k full or 2^k-1 fractional factorials. In this dissertation, a more general mathematical formulation of the Box-Behnken method is provided, a general expression for the coefficient matrix in the least squares analysis for estimating the parameters in the second order model is derived, and the properties of Box-Behnken designs with respect to the estimability of all parameters in a second-order model are investigated when 2^kfull factorials are used. The results show that for all pure quadratic coefficients to be estimable, the PBIB(m) design has to be chosen such that its incidence matrix is of full rank, and for all mixed quadratic coefficients to be estimable the PBIB(m) design has to be chosen such that the parameters λ₁, λ₂, ...,λ_m are all greater than zero. In order to reduce the number of experimental points the use of 2^k-1 fractional factorials instead of 2^k full factorials is being considered. Of particular interest and importance are separate considerations of fractions of resolutions III, IV, and V. The construction of Box-Behnken designs using such fractions is described and the properties of the designs concerning estimability of regression coefficients are investigated. Using designs obtained from resolution V factorials have the same properties as those using full factorials. Resolutions III and IV designs may lead to non-estimability of certain coefficients and to correlated estimators. The final topic is concerned with Box-Behnken designs in which treatments are applied to experimental units sequentially in time or space and in which there may exist a linear trend effect. For this situation, one wants to find appropriate run orders for obtaining a linear trend-free Box-Behnken design to remove a linear trend effect so that a simple technique, analysis of variance, instead of a more complicated technique, analysis of covariance, to remove a linear trend effect can be used. Construction methods for linear trend-free Box-Behnken designs are introduced for different values of block size (for the underlying PBIB design) k. For k= 2 or 3, it may not always be possible to find linear trend-free Box-Behnken designs. However, for k ≥ 4 linear trend-free Box-Behnken designs can always be constructed. / Ph. D.

Despite considerable advances in missing data imputation methods over the last three decades, the problem of missing data remains largely unsolved. Many techniques have emerged in the literature as candidate solutions. These techniques can be categorised into two classes: statistical methods of data imputation and computational intelligence methods of data imputation. Due to the longstanding use of statistical methods in handling missing data problems, it takes quite some time for computational intelligence methods to gain profound attention even though these methods have analogous accuracy, in comparison to other approaches. The merits of both these classes have been discussed at length in the literature, but only limited studies make significant comparison to these classes. This thesis contributes to knowledge by firstly, conducting a comprehensive comparison of standard statistical methods of data imputation, namely, mean substitution (MS), regression imputation (RI), expectation maximization (EM), tree imputation (TI) and multiple imputation (MI) on missing completely at random (MCAR) data sets. Secondly, this study also compares the efficacy of these methods with a computational intelligence method of data imputation, ii namely, a neural network (NN) on missing not at random (MNAR) data sets. The significance difference in performance of the methods is presented. Thirdly, a novel procedure for handling missing data is presented. A hybrid combination of each of these statistical methods with a NN, known here as the post-processing procedure, was adopted to approximate MNAR data sets. Simulation studies for each of these imputation approaches have been conducted to assess the impact of missing values on partial least squares structural equation modelling (PLS-SEM) based on the estimated accuracy of both structural and measurement parameters. The best method to deal with particular missing data mechanisms is highly recognized. Several significant insights were deduced from the simulation results. It was figured that for the problem of MCAR by using statistical methods of data imputation, MI performs better than the other methods for all percentages of missing data. Another unique contribution is found when comparing the results before and after the NN post-processing procedure. This improvement in accuracy may be resulted from the neural network¿s ability to derive meaning from the imputed data set found by the statistical methods. Based on these results, the NN post-processing procedure is capable to assist MS in producing significant improvement in accuracy of the approximated values. This is a promising result, as MS is the weakest method in this study. This evidence is also informative as MS is often used as the default method available to users of PLS-SEM software. / Minister of Higher Education Malaysia and University Utara Malaysia

Pairwise comparison matrices (PCMs) play a key role in multi-criteria decision making, especially in the analytic hierarchy process. It could be necessary for an expert to compare alternatives based on various criteria. However, for a variety of reasons, such as lack of time or insufficient knowledge, it may happen that the expert cannot provide judgments on all pairs of alternatives. In this case, an incomplete pairwise comparison matrix is formed. In the first research part, an optimization algorithm is proposed for the optimal completion of an incomplete PCM. It is intended to numerically minimize a constrained eigenvalue problem, in which the objective function is difficult to write explicitly in terms of variables. Numerical simulations are carried out to examine the performance of the algorithm. The simulation results show that the proposed algorithm is capable of solving the minimization of the constrained eigenvalue problem. In the second part, a comparative analysis of eleven completion methods is studied. The similarity of the eleven completion methods is analyzed on the basis of numerical simulations and hierarchical clustering. Numerical simulations are performed for PCMs of different orders considering various numbers of missing comparisons. The results suggest the existence of a cluster of five extremely similar methods, and a method significantly dissimilar from all the others. In the third part, the filling in patterns (arrangements of known comparisons) of incomplete PCMs based on their graph representation are investigated under given conditions: regularity, diameter and number of vertices, but without prior information. Regular and quasi-regular graphs with minimal diameter are proposed. Finally, the simulation results indicate that the proposed graphs indeed provide better weight vectors than alternative graphs with the same number of comparisons. This research problem’s contributions include a list of (quasi-)regular graphs with diameters of 2 and 3, and vertices from 5 up to 24.

Silicon carbide is a very promising semiconductor material for high-power, highrequency, and high-temperature applications. SiC distinguishes from traditional narrow bandgap semiconductors, such as silicon, in that common doping impurities in SiC have activation energies larger than the thermal energy kT even at room temperature. This causes incomplete ionization of such impurities, which leads to strong temperature and frequency dependence of the semiconductor junction differential admittance and, if carrier freeze-out effects are not taken into account, errors in doping profiles calculated from capacitance-voltage data. Approaches commonly used to study the influence of incomplete impurity ionization on the junction admittance are based on the truncated space charge approximation and/or the small-signal approximation. The former leads to impurity ionization time constant and occupation number errors, while the latter fails if the measurement ac signal amplitude is larger than kT/q. In this work, a new reverse bias Schottky junction admittance model valid for the general case of an arbitrary temperature, measurement signal frequency and amplitude, and doping occupation number and time constant distributions is developed. Results of junction admittance calculations using the developed model are compared with the results of traditional models. Based on the general model, a new method of admittance spectroscopy data analysis is created and used to determine impurity parameters more accurately than allowed by traditional approaches. Incomplete impurity ionization is investigated for the case of nitrogen donors and aluminum and boron acceptors in 4H- and 6H-SiC. It is shown that the degree of carrier freeze-out is significant in heavily N-doped 6H-SiC and in Al- and B-doped SiC. Frequency dispersion of the junction admittance is shown to be significant at room temperature in N- and B-doped SiC. Junction capacitance calculations as a function of applied dc bias show that calculated doping profiles deviate from the actual impurity concentration profiles if the impurity ionization time constant is comparable with the ac signal period. This is the case for N- and B-doped SiC with certain values of the impurity activation energy and capture cross-section. Validity of the new model and its predictions are successfully tested on experimental admittance data for N- and B-doped SiC Schottky diodes.

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